Use case · Product recommendations

Personal recommendations, built for every shopper.

Stop dumping the catalog. Ask the right two questions, read the signals, and recommend the one product they're most likely to buy.

UserHelp me pick sunglasses for a round face.
Frame & Co
Style advisor
Online
Top match for you
Aviator Slim — Tortoise
$1294.8
The problem

Why it isn't working right now.

82%
of shoppers want recommendations

But only 22% of stores deliver anything personalized beyond “best-sellers.”

30+
filters before any result

Every extra click is a dropped customer. Filtering UIs scale linearly with frustration.

61%
leave when overwhelmed

Endless grids beat no grid — but a single confident pick beats both.

How it works

Four steps. No engineering.

2–5 min
01

Connect your catalog

Plug into Shopify, WooCommerce, or any sitemap. Variants, tags and reviews come along.

5–30 min
02

Learn the shopper signals

Embeddings on every product + page-view context = real understanding of intent.

Same day
03

Recommend in chat

Two clarifying questions, one confident pick. Rich product cards inline.

Compounding
04

Tune over time

See which recommendations convert, which get ignored, and which products are missing.

Example

From “I'm not sure” to checkout

A first-time visitor with a vague brief gets a confident, justified pick in two messages.

Live conversation

From “I'm not sure” to checkout

A first-time visitor with a vague brief gets a confident, justified pick in two messages.

Avg response 1.6s
Looking for a gift for my mom — she likes citrus and travels a lot.
Got it. Travel-friendly + citrus narrows it nicely. One question: is she more “fresh-clean” or “sweet-warm”?
Fresh-clean for sure.
Bergamot Travel Set
$744.9
Top citrus pick — TSA-friendly format, our #1 gift for moms this month.
Yuzu Roll-on
$484.9
Cleaner profile if she wants something lighter.
Want me to add a hand-written note?
Results
Add-to-cart
+71%
from recommended cards
Time to decision
−54%
vs. browsing filters
Repeat buyers
+22%
3-month cohort
Catalog coverage
3.4×
long-tail SKUs exposed
What's inside

Built to do the work.

Two-question funnels

The assistant asks just enough to narrow — never an interrogation.

Context-aware

It knows what they looked at, what's in cart, and what's on sale.

Ranked picks

Always one clear top pick plus 1–2 alternatives — never a wall.

Variant-aware

Size, color, finish — the suggestion respects what's actually available.

Justification

Every recommendation includes a sentence on why it fits.

Click-through analytics

See which recommendations win, by product, persona and channel.

Common questions

  • Helpful but not required. The assistant uses descriptions, reviews and category structure to fill gaps.

Ready when you are

One confident pick beats a wall of options.

14 days · No credit card · Live in 10 minutes

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